There have been two audio tagging tasks in previous DCASE editions, each focused on a specific domain of sounds.
In DCASE 2016 [1]}, the task targeted domestic audio tagging for which the CHiME-Home dataset was used, including 7 sound categories and 6.8h of recordings [2]}.
In DCASE 2017 [3]}, the task focused on audio tagging in the context of smart cars, for which a large-scale dataset featuring 17 categories was utilized.
Other popular datasets for sound event classification are the ESC-50 [4]} and the UrbanSound8k [5]} datasets.
The former counts with 50 diverse categories but less than 3h of audio recordings.
The latter is designed to enable urban sound research, featuring 10 categories and almost 9h of audio.
Hence, many of available data resources for sound event classification are domain-specific, and/or of relatively small size.
Recently, however, general-purpose sound event recognizers have gained attention, where a wide range of sounds events are considered, not tied to a specific domain.
This research has been mostly triggered by AudioSet, a large-scale audio dataset structured with an ontology of 632 sound events [6]}.
